News & Research

Comprehensive year-in-review of human data rights developments in 2026, including landmark legislation, major settlements, emerging technologies, and the path forward.

A comprehensive analysis of the EU AI Act's data rights requirements, transparency obligations, and what they mean for individuals and organizations in the age of artificial intelligence.

New research reveals AI needs only 5-7x current automation productivity to fund an 11% GDP universal basic income. Analysis of the economic thresholds, timelines, and policy implications.

Analysis of the landmark Bartz v. Anthropic settlement and what it means for consent-based frameworks, AI training data rights, and creator compensation in the AI industry.

Step-by-step guide to protecting your personal data and creative works from AI training, including opt-out mechanisms, technical measures, and legal rights you can exercise today.

Comprehensive analysis of the EU Data Act's September 2025 implementation, its impact on IoT data, industrial data portability, and what it means for human data rights.

A comprehensive retrospective on GDPR's impact since 2018, enforcement trends, and critical lessons for protecting data rights as AI transforms the digital landscape.

Analysis of the Colorado Algorithmic Accountability Act, the first comprehensive US state law requiring transparency and accountability in AI decision-making systems.

Comprehensive comparison of AI governance approaches across the US, EU, UK, China, Japan, and other major jurisdictions, analyzing data rights implications and international convergence.

Exploring the economic models for fair data compensation in the AI era, from direct payments to data dividends, usage-based pricing, and collective licensing frameworks.

Analysis of the AI Dividend pilot program's first year, examining the impact of $1,000 monthly payments on workers affected by AI and what it teaches us about data compensation.

Deep analysis of broken data provenance in AI development, based on landmark ICML 2024 research. Understanding consent, authenticity, and accountability in AI training data.

Technical analysis of machine unlearning research, examining whether data can truly be removed from trained AI models and what this means for privacy and data rights.

How federated learning enables AI development without centralizing data, its potential for protecting data rights, and current limitations of privacy-preserving AI approaches.

Tracing the origins of the human data rights movement from early privacy advocacy through the AI era, examining key milestones, coalition building, and the vision for a fair data economy.